Cervical cancer is a cancer arising from a cervix of a female. The conventional way of cervical-cancer screening is to visually examine cervical cells on a slide through a microscope by a cytotechnologist to check for any cell showing signs of malignant changes. Typically, about 100,000 cells in a single sample are required to be examined. This examination process takes around 10-15 minutes and is thus time-consuming and costly.
To reduce time and cost, computer-aided automatic cancer screening is particularly useful. Due to a high accuracy in various image-classification tasks, a CNN has been used for automatic cervical-cancer screening, e.g., in U.S. Pat. No. 9,739,783 and CN106991673. However, training the CNN with adversarial samples has been shown to result in significant impairment of the CNN classification performance. Each adversarial sample is an image containing a cervical cell that is classified and labeled, and further including a number of irrelevant objects, such as a noisy background, irrelevant cells, micro-organisms, or even cells with opposite labels in the background. FIG. 1 is a real-life adversarial sample as an example for illustration. A training image 100, which is used for training a CNN, has an abnormal cell 110 as a principal pre-classified object for training the CNN. In the vicinity of the abnormal cell 110, there are nearby normal cells 120, 121 and micro-organisms 130, 131. The normal cells 120, 121 and the micro-organisms 130, 131 are irrelevant objects that interfere the training process of the CNN. These irrelevant objects could make the CNN learn incorrect features, thereby leading to misclassification.
It is desirable to have a technique that enhances the chance of successful classification of abnormal cells by a CNN in the presence of interfering irrelevant objects.